Data Science and Analytics with Python
Chapman & Hall/CRC (Verlag)
978-1-4987-4209-2 (ISBN)
Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python, such as SciKit-learn, Pandas, Numpy, and others. The use of Python is of particular interest, given its recent popularity in the data science community. The book can be used by seasoned programmers and newcomers alike.
The book is organized in a way that individual chapters are sufficiently independent from each other so that the reader is comfortable using the contents as a reference. The book discusses what data science and analytics are, from the point of view of the process and results obtained. Important features of Python are also covered, including a Python primer. The basic elements of machine learning, pattern recognition, and artificial intelligence that underpin the algorithms and implementations used in the rest of the book also appear in the first part of the book.
Regression analysis using Python, clustering techniques, and classification algorithms are covered in the second part of the book. Hierarchical clustering, decision trees, and ensemble techniques are also explored, along with dimensionality reduction techniques and recommendation systems. The support vector machine algorithm and the Kernel trick are discussed in the last part of the book.
About the Author
Dr. Jesús Rogel-Salazar
is a Lead Data scientist with experience in the field working for companies such as AKQA, IBM Data Science Studio, Dow Jones and others. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK, He obtained his doctorate in physics at Imperial College London for work on quantum atom optics and ultra-cold matter. He has held a position as senior lecturer in mathematics as well as a consultant in the financial industry since 2006. He is the author of the book Essential Matlab and Octave, also published by CRC Press. His interests include mathematical modelling, data science, and optimization in a wide range of applications including optics, quantum mechanics, data journalism, and finance.
Dr. Jesús Rogel-Salazar is a Lead Data Scientist at IBM Data Science Studio and visiting researcher at the Department of Physics at Imperial College London, UK. He is also a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK. He obtained his doctorate in Physics at Imperial College London for work on quantum atom optics and ultra-cold matter. He has held a position as senior lecturer in mathematics as well as a consultant and data scientist in the financial industry since 2006. He is the author of the book “Essential Matlab and Octave”, also published with CRC Press. His interests include mathematical modelling, data science and optimisation in a wide range of applications including optics, quantum mechanics, data journalism and finance. Dr. Jesús Rogel-Salazar is a Lead Data Scientist at IBM Data Science Studio and visiting researcher at the Department of Physics at Imperial College London, UK. He is also a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK. He obtained his doctorate in Physics at Imperial College London for work on quantum atom optics and ultra-cold matter. He has held a position as senior lecturer in mathematics as well as a consultant and data scientist in the financial industry since 2006. He is the author of the book “Essential Matlab and Octave”, also published with CRC Press. His interests include mathematical modelling, data science and optimisation in a wide range of applications including optics, quantum mechanics, data journalism and finance.
The Trials and Tribulations of a Data Scientist. Python: For Something Completely Different. The Machine that Goes "Ping": Machine Learning and Pattern Recognition. The Relationship Conundrum: Regression. Jackalopes and Hares: Clustering. Decisions, Decisions: Hierarchical Clustering, Decision Trees and Ensable Techniques. Less is More: Dimensionality Reduction. Kernel Tricks under the Sleeve: Support Vector Machines. Pipelines in Scikit-learn.
| Erscheint lt. Verlag | 1.10.2017 |
|---|---|
| Reihe/Serie | Chapman & Hall/CRC Data Mining and Knowledge Discovery Series |
| Zusatzinfo | 19 Tables, black and white; 25 Illustrations, black and white |
| Sprache | englisch |
| Maße | 191 x 235 mm |
| Gewicht | 746 g |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge | |
| Mathematik / Informatik ► Informatik ► Software Entwicklung | |
| Mathematik / Informatik ► Informatik ► Theorie / Studium | |
| ISBN-10 | 1-4987-4209-2 / 1498742092 |
| ISBN-13 | 978-1-4987-4209-2 / 9781498742092 |
| Zustand | Neuware |
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